Model-based analysis of oligonucleotide arrays: Expression index computation and outlier detection.
Open accessAbstract
Recent advances in cDNA and oligonucleotide DNA arrays have made it possible to measure the abundance of mRNA transcripts for many genes simultaneously. The analysis of such experiments is nontrivial because of large data size and many levels of variation introduced at different stages of the experiments. The analysis is further complicated by the large differences that may exist among different probes used to interrogate the same gene. However, an attractive feature of high-density oligonucleotide arrays such as those produced by photolithography and inkjet technology is the standardization of chip manufacturing and hybridization process. As a result, probe-specific biases, although significant, are highly reproducible and predictable, and their adverse effect can be reduced by proper modeling and analysis methods. Here, we propose a statistical model for the probe-level data, and develop model-based estimates for gene expression indexes. We also present model-based methods for identifying and handling cross-hybridizing probes and contaminating array regions. Applications of these results will be presented elsewhere.
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The reliability of the clusters was verified by a resampling method based on standard errors, repeated 100 times.
“The reliability of the clusters was verified by a resampling method based on the standard errors for expression values [30]. This subsampling procedure was repeated 100 times to refine the cluster parameters.”
From A HIF1α Regulatory Loop Links Hypoxia and Mitochondrial Signals in Pheochromocytomas (2005) · cites this paper as [30]
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- Authors
- Li, C., & Wong, W. H.
- Affiliations
- University of California, Los Angeles (US)
- Corresponding author
- Wong, Wing Hung
- ORCID
- Wong, Wing Hung
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2000-12-26
- Volume
- 98
- Issue
- 1
- Pages
- 31-36
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC14539
- OpenAlex
- W2038437697
- Cited by
- 3,133
- References
- 8
- Citations by year
- 2026: 3 · 2025: 9 · 2024: 6 · 2023: 20 · 2022: 16 · 2021: 66 · 2020: 20 · 2019: 28 · 2018: 42 · 2017: 57 · 2016: 71 · 2015: 87 · 2014: 106 · 2013: 148 · 2012: 146
- FWCI
- 30.28
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Human Genome Research Institute (1R01HG02341-01, R01HG002341)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Molecular Biology Techniques and Applications
- Keywords
- oligonucleotide microarrays, outlier detection, cross-hybridization, cDNA microarray
- MeSH terms
- Animals, Humans, Nucleic Acid Hybridization, Reference Standards, Research Design, RNA, Messenger, Models, Statistical, DNA Probes, Artifacts, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling, Mice
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